Building detection from very high resolution (VHR) optical remote sensing images, which is
an essential but challenging task in remote sensing, has attracted increased attention in …

In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …

Sudden-onset natural and man-made disasters represent a threat to the safety of human life
and property. Rapid and accurate building damage assessment using bitemporal high …

Building footprint information is one foundation for understanding urban processes and
hence a program for environmentally sustainable urbanization. For most cities, municipal …

Deep learning methods have achieved considerable progress in remote sensing image
building extraction. Most building extraction methods are based on Convolutional Neural …

We present PolyBuilding, a polygon Transformer for building extraction. PolyBuilding direct
predicts vector representation of buildings from remote sensing images. It builds upon an …

This paper studies the problem of the polygonal mapping of buildings by tackling the issue
of mask reversibility, which leads to a notable performance gap between the predicted …

Fast and effective responses are required when a natural disaster (eg, earthquake and
hurricane) strikes. Building damage assessment from satellite imagery is critical before relief …

Automatic extraction of building instances from high spatial resolution optical remote
sensing imagery is essential for urban infrastructure and smart management. In view of the …

Building extraction is a challenging research direction in remote sensing image (RSI)
interpretation. Due to the fact that a building has not only its own local structures but also …